Leveraging triplet loss and nonlinear dimensionality reduction for on-the-fly channel charting

Autor:	Yassine, Taha, Magoarou, Luc Le, Paquelet, Stéphane, Crussière, Matthieu
Rok vydání:	2022
Předmět:	Computer Science - Networking and Internet Architecture Computer Science - Machine Learning Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
Popis:	Channel charting is an unsupervised learning method that aims at mapping wireless channels to a so-called chart, preserving as much as possible spatial neighborhoods. In this paper, a model-based deep learning approach to this problem is proposed. It builds on a physically motivated distance measure to structure and initialize a neural network that is subsequently trained using a triplet loss function. The proposed structure exhibits a low number of parameters and clever initialization leads to fast training. These two features make the proposed approach amenable to on-the-fly channel charting. The method is empirically assessed on realistic synthetic channels, yielding encouraging results.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2204.13996 Zobrazit plný text záznamu View this record from Arxiv